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Can an automated personalized nutrition assistance system successfully change nutrition behavior? - Study design

  • Technical University of Munich

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Despite a multitude of existing dietary guidelines, the rise of the number of people suffering from a diet-related disease occurs on a yearly basis. Studies show that the response to different diets varies individually, calling for more personalized measures available at any time in any context. Therefore, this paper proposes a research design based on a smartphone app, that delivers automated, personalized dietary recommendations, to encourage a healthier nutrition lifestyle. Founding on previous research in computer and nutritional science, we propose 6 different intervention factors: (1) type of dietary recommendations, (2) dietary assessment, (3) tracking of physical activity via smartphones or smart activity trackers, (4) feedback with visualization of personal nutritional data, (5) feedback with textual explanations behind recommendations, and (6) dietary recommendations including blood values. In an extensive 6-month field study, we plan to examine which of the factors influence a healthier behavior change and long-term app engagement most.

Original languageEnglish
Title of host publication2016 International Conference on Information Systems, ICIS 2016
PublisherAssociation for Information Systems
ISBN (Electronic)9780996683135
StatePublished - 2016
Event2016 International Conference on Information Systems, ICIS 2016 - Dublin, Ireland
Duration: 11 Dec 201614 Dec 2016

Publication series

Name2016 International Conference on Information Systems, ICIS 2016

Conference

Conference2016 International Conference on Information Systems, ICIS 2016
Country/TerritoryIreland
CityDublin
Period11/12/1614/12/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 2 - Zero Hunger
    SDG 2 Zero Hunger

Keywords

  • Behavior change
  • Experimental design
  • Healthy eating
  • M-Health
  • Personalized nutrition
  • Recommender systems
  • Study design
  • Visualization

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